



'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: Tuple									=      LDMTextToImagePipeline
			a_	: Optional[Any]									=      TEXT_TO_IMAGE_PARAMS - {
			    '''negative_prompt''',
			    '''negative_prompt_embeds''',
			    '''cross_attention_kwargs''',
			    '''prompt_embeds''',
			}
			a_	: str									=      PipelineTesterMixin.required_optional_params - {
			    '''num_images_per_prompt''',
			    '''callback''',
			    '''callback_steps''',
			}
			a_	: Union[str, Any]									=      TEXT_TO_IMAGE_BATCH_PARAMS
			a_	: Union[str, Any]									=      False






			def 						_lowercase  (     self						)							->							Optional[int]:
					torch.manual_seed(0						)
					snake_case__	     =UNetaDConditionModel(
					    block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=4					,   out_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,   cross_attention_dim=32					,   )
					snake_case__	     =DDIMScheduler(
					    beta_start=0.00_085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=_UpperCAmelCase					,   set_alpha_to_one=_UpperCAmelCase					,   )
					torch.manual_seed(0						)
					snake_case__	     =AutoencoderKL(
					    block_out_channels=(32, 64)					,   in_channels=3					,   out_channels=3					,   down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')					,   up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')					,   latent_channels=4					,   )
					torch.manual_seed(0						)
					snake_case__	     =CLIPTextConfig(
					    bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )
					snake_case__	     =CLIPTextModel(_UpperCAmelCase						)
					snake_case__	     =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

					snake_case__	     ={
					    'unet': unet,
					    'scheduler': scheduler,
					    'vqvae': vae,
					    'bert': text_encoder,
					    'tokenizer': tokenizer,
					}
					return components






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=0						)							->							Optional[int]:
					if str(_UpperCAmelCase						).startswith('mps'						):
							snake_case__	     =torch.manual_seed(_UpperCAmelCase						)
					else:
							snake_case__	     =torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
					snake_case__	     ={
					    'prompt': 'A painting of a squirrel eating a burger',
					    'generator': generator,
					    'num_inference_steps': 2,
					    'guidance_scale': 6.0,
					    'output_type': 'numpy',
					}
					return inputs






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     ='cpu'  # ensure determinism for the device-dependent torch.Generator

					snake_case__	     =self.get_dummy_components()
					snake_case__	     =LDMTextToImagePipeline(**_UpperCAmelCase						)
					pipe.to(_UpperCAmelCase						)
					pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

					snake_case__	     =self.get_dummy_inputs(_UpperCAmelCase						)
					snake_case__	     =pipe(**_UpperCAmelCase						).images
					snake_case__	     =image[0, -3:, -3:, -1]

					assert image.shape == (1, 16, 16, 3)
					snake_case__	     =np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]						)

					assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3





@slow
@require_torch_gpu
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							List[str]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=torch.floataa					,   _UpperCAmelCase=0						)							->							int:
					snake_case__	     =torch.manual_seed(_UpperCAmelCase						)
					snake_case__	     =np.random.RandomState(_UpperCAmelCase						).standard_normal((1, 4, 32, 32)						)
					snake_case__	     =torch.from_numpy(_UpperCAmelCase						).to(device=_UpperCAmelCase					,   dtype=_UpperCAmelCase						)
					snake_case__	     ={
					    'prompt': 'A painting of a squirrel eating a burger',
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 3,
					    'guidance_scale': 6.0,
					    'output_type': 'numpy',
					}
					return inputs






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'						).to(_UpperCAmelCase						)
					pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

					snake_case__	     =self.get_inputs(_UpperCAmelCase						)
					snake_case__	     =pipe(**_UpperCAmelCase						).images
					snake_case__	     =image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 256, 256, 3)
					snake_case__	     =np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]						)
					snake_case__	     =np.abs(expected_slice - image_slice						).max()
					assert max_diff < 1E-3





@nightly
@require_torch_gpu
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							int:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=torch.floataa					,   _UpperCAmelCase=0						)							->							int:
					snake_case__	     =torch.manual_seed(_UpperCAmelCase						)
					snake_case__	     =np.random.RandomState(_UpperCAmelCase						).standard_normal((1, 4, 32, 32)						)
					snake_case__	     =torch.from_numpy(_UpperCAmelCase						).to(device=_UpperCAmelCase					,   dtype=_UpperCAmelCase						)
					snake_case__	     ={
					    'prompt': 'A painting of a squirrel eating a burger',
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 50,
					    'guidance_scale': 6.0,
					    'output_type': 'numpy',
					}
					return inputs






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'						).to(_UpperCAmelCase						)
					pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

					snake_case__	     =self.get_inputs(_UpperCAmelCase						)
					snake_case__	     =pipe(**_UpperCAmelCase						).images[0]

					snake_case__	     =load_numpy(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'						)
					snake_case__	     =np.abs(expected_image - image						).max()
					assert max_diff < 1E-3









'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




